This put up is co-written with Mayur Patel, Nick Koenig, and Karthik Jetti from GoDaddy.
GoDaddy empowers on a regular basis entrepreneurs by offering all the assistance and instruments to succeed on-line. With 21 million clients worldwide, GoDaddy’s world options assist seamlessly join entrepreneurs’ id and presence with commerce, resulting in worthwhile development. At GoDaddy, we take pleasure in being a data-driven firm. Our relentless pursuit of useful insights from information fuels our enterprise selections and works to realize buyer satisfaction.
On this put up, we talk about how GoDaddy’s Care & Providers crew, in shut collaboration with the AWS GenAI Labs crew, constructed Lighthouse—a generative AI resolution powered by Amazon Bedrock. Amazon Bedrock is a completely managed service that makes basis fashions (FMs) from main AI startups and Amazon accessible by way of an API, so you may select from a variety of FMs to seek out the mannequin that’s greatest suited in your use case. With the Amazon Bedrock serverless expertise, you will get began shortly, privately customise FMs with your individual information, and combine and deploy them into your functions utilizing the AWS instruments with out having to handle infrastructure. With Amazon Bedrock, GoDaddy’s Lighthouse mines insights from buyer care interactions utilizing crafted prompts to determine prime name drivers and scale back friction factors in clients’ product and web site experiences, resulting in improved buyer expertise.
GoDaddy’s enterprise problem
Information has all the time been a aggressive benefit for GoDaddy, as has the Care & Providers crew . We understand the potential to derive significant insights from this information and determine key name drivers and ache factors. On this planet earlier than generative AI, nonetheless, the expertise for mining insights from unstructured information was computationally costly and difficult to operationalize.
Answer overview
This modified with GoDaddy Lighthouse, a generative AI-powered interactions analytics resolution, which unlocks the wealthy mine of insights sitting inside our buyer care transcript information. Fed by buyer care interactions information, it allows scale for deep and actionable evaluation, permitting us to:
- Detect and measurement buyer friction factors in our product and web site experiences, resulting in enhancements in buyer expertise (CX) and retention
- Enhance buyer care operations, together with high quality assurance and routing optimization, resulting in enhancements in CX and operational expenditure (OpEx)
- Deprecate our reliance on expensive vendor options for voice analytics
The next diagram illustrates the high-level enterprise workflow of Lighthouse.
GoDaddy Lighthouse is an insights resolution powered by giant language fashions (LLMs) that permits immediate engineers all through the corporate to craft, handle, and consider prompts utilizing a portal the place they’ll work together with an LLM of their alternative. By engineering prompts that run in opposition to an LLM, we are able to systematically derive highly effective and standardized insights throughout text-based information. Product material specialists use the Lighthouse platform UI to check and iterate on generative AI prompts that produce tailor-made insights a couple of Care & Providers interplay.
The under diagram exhibits the iterative course of of making and strengthening the prompts.
After the prompts are examined and confirmed to work as meant, they’re deployed into manufacturing, the place they’re scaled throughout hundreds of interactions. Then, the insights produced for every interplay are aggregated and visualized in dashboards and different analytical instruments. Moreover, Lighthouse lets GoDaddy customers craft one-time generative AI prompts to disclose wealthy insights for a extremely particular buyer situation.
Let’s dive into how the Lighthouse structure and options assist customers in producing insights. The next diagram illustrates the Lighthouse structure on AWS.
The Lighthouse UI is powered by information generated from Amazon Bedrock LLM calls on hundreds of transcripts, using a library of prompts from GoDaddy’s inside immediate catalog. The UI facilitates the collection of LLM mannequin based mostly on the person’s alternative, making the answer unbiased of 1 mannequin. These LLM calls are processed sequentially utilizing Amazon EMR and Amazon EMR Serverless. The seamless integration of backend information into the UI is facilitated by Amazon API Gateway and Amazon Lambdas features, whereas the UI/UX is supported by AWS Fargate and Elastic Load Balancing to keep up excessive availability. For information storage and retrieval, Lighthouse employs a mixture of Amazon DynamoDB, Amazon Easy Storage Service (Amazon S3), and Amazon Athena. Visible information evaluation and illustration are achieved by way of dashboards constructed on Tableau and Amazon QuickSight.
Immediate analysis
Lighthouse presents a singular proposition by permitting customers to guage their one-time generative AI prompts utilizing an LLM of their alternative. This characteristic empowers customers to put in writing a brand new one-time immediate particularly for analysis functions. Lighthouse processes this new immediate utilizing the precise transcript and response from a earlier LLM name.
This functionality is especially useful for customers in search of to refine their prompts by way of a number of iterations. By iteratively adjusting and evaluating their prompts, customers can progressively improve and solidify the effectiveness of their queries. This iterative refinement course of makes positive that customers can obtain the highest-quality outputs tailor-made to their particular wants.
The flexibleness and precision provided by this characteristic make Lighthouse an indispensable software for anybody attempting to optimize their interactions with LLMs, fostering steady enchancment and innovation in immediate engineering.
The next screenshot illustrates how Lighthouse lets customers validate the accuracy of the mannequin response with an analysis immediate
After a immediate is evaluated for high quality and the person is happy with the outcomes, the immediate could be promoted into the immediate catalog.
Response summarization
After the person submits their immediate, Lighthouse processes this immediate in opposition to every accessible transcript, producing a collection of responses. The person can then view the generated responses for that question on a devoted web page. This web page serves as a useful useful resource, permitting customers to assessment the responses intimately and even obtain them into an Excel sheet for additional evaluation.
Nonetheless, the sheer quantity of responses can typically make this process overwhelming. To deal with this, Lighthouse presents a characteristic that permits customers to move these responses by way of a brand new immediate for summarization. This performance allows customers to acquire concise, single-line summaries of the responses, considerably simplifying the assessment course of and enhancing effectivity.
The next screenshot exhibits an instance of the immediate with which Lighthouse lets customers meta-analyze all responses into one, decreasing the time wanted to assessment every response individually.
With this summarization software, customers can shortly distill giant units of knowledge into simply digestible insights, streamlining their workflow and making Lighthouse an indispensable software for information evaluation and decision-making.
Insights
Lighthouse generates useful insights, offering a deeper understanding of key focus areas, alternatives for enchancment, and strategic instructions. With these insights, GoDaddy could make knowledgeable, strategic selections that improve operational effectivity and drive income development.
The next screenshot is an instance of the dashboard based mostly on insights generated by Lighthouse, exhibiting the distribution of classes in every perception.
By way of Lighthouse, we analyzed the distribution of root causes and intents throughout the huge variety of day by day calls dealt with by GoDaddy brokers. This evaluation recognized probably the most frequent causes of escalations and elements probably to result in buyer dissatisfaction.
Enterprise worth and influence
To this point (as of the time of writing), Lighthouse has generated 15 new insights. Most notably, the crew used insights from Lighthouse to quantify the influence and price of the friction inside the present course of, enabling them to prioritize crucial enhancements throughout a number of departments. This strategic strategy led to a streamlined password reset course of, decreasing assist contacts associated to the password reset course of and shortening decision instances, finally offering important value financial savings.
Different insights resulting in enhancements to the GoDaddy enterprise embrace:
- The invention of name routing flows suboptimal to revenue per interplay
- Understanding the basis reason behind repeat contact interactions
Conclusion
GoDaddy’s Lighthouse, powered by Amazon Bedrock, represents a transformative leap in utilizing generative AI to unlock the worth hidden inside unstructured buyer interplay information. By scaling deep evaluation and producing actionable insights, Lighthouse empowers GoDaddy to reinforce buyer experiences, optimize operations, and drive enterprise development. As a testomony to its success, Lighthouse has already delivered monetary and operational enhancements, solidifying GoDaddy’s place as a data-driven chief within the trade.
Concerning the Authors
Mayur Patel is a Director, Software program Improvement within the Information & Analytics (DnA) crew at GoDaddy, specializing in information engineering and AI-driven options. With almost 20 years of expertise in engineering, structure, and management, he has designed and carried out modern options to enhance enterprise processes, scale back prices, and enhance income. His work has enabled firms to realize their highest potential by way of information. Obsessed with leveraging information and AI, he goals to create options that delight clients, improve operational effectivity, and optimize prices. Exterior of his skilled life, he enjoys studying, climbing, DIY tasks, and exploring new applied sciences.
Nick Koenig is a Senior Director of Information Analytics and has labored throughout GoDaddy constructing information options for the final 10 years. His first job at GoDaddy included listening to calls and discovering traits, so he’s significantly proud to be concerned in constructing an AI resolution for this a decade later.
Karthik Jetti is a Senior Information Engineer within the Information & Analytics group at GoDaddy. With greater than 12 years of expertise in engineering and structure in information applied sciences, AI, and cloud platforms, he has produced information to assist superior analytics and AI initiatives. His work drives technique and innovation, specializing in income technology and bettering effectivity.
Ranjit Rajan is a Principal GenAI Lab Options Architect with AWS. Ranjit works with AWS clients to assist them design and construct information and analytics functions within the cloud.
Satveer Khurpa is a Senior Options Architect on the GenAI Labs crew at Amazon Net Providers. On this function, he makes use of his experience in cloud-based architectures to develop modern generative AI options for purchasers throughout various industries. Satveer’s deep understanding of generative AI applied sciences permits him to design scalable, safe, and accountable functions that unlock new enterprise alternatives and drive tangible worth.
Richa Gupta is a Options Architect at Amazon Net Providers specializing in generative AI and AI/ML designs. She helps clients implement scalable, cloud-based options to make use of superior AI applied sciences and drive enterprise development. She has additionally offered generative AI use circumstances in AWS Summits. Previous to becoming a member of AWS, she labored within the capability of a software program engineer and options architect, constructing options for big telecom operators.
